telnyx-ai-inference-python

Original：🇺🇸 English
Translated

Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides Python SDK examples.

1installs
Sourceteam-telnyx/telnyx-ext-agent-skills
Added on

NPX Install

npx skill4agent add team-telnyx/telnyx-ext-agent-skills telnyx-ai-inference-python

Tags

Translated version includes tags in frontmatter
telnyx-ai-sdkllm-inferenceai-embeddingschat-completionspeech-transcription

SKILL.md Content

View Translation Comparison →
<!-- Auto-generated from Telnyx OpenAPI specs. Do not edit. -->

Telnyx Ai Inference - Python

Installation

bash
pip install telnyx

Setup

python
import os
from telnyx import Telnyx

client = Telnyx(
    api_key=os.environ.get("TELNYX_API_KEY"),  # This is the default and can be omitted
)
All examples below assume 
client
is already initialized as shown above.

List conversations

Retrieve a list of all AI conversations configured by the user.
GET /ai/conversations
python
conversations = client.ai.conversations.list()
print(conversations.data)

Create a conversation

Create a new AI Conversation.
POST /ai/conversations
Optional: 
metadata
(object), 
name
(string)
python
conversation = client.ai.conversations.create()
print(conversation.id)

Get Insight Template Groups

Get all insight groups
GET /ai/conversations/insight-groups
python
page = client.ai.conversations.insight_groups.retrieve_insight_groups()
page = page.data[0]
print(page.id)

Create Insight Template Group

Create a new insight group
POST /ai/conversations/insight-groups
— Required: 
name
Optional: 
description
(string), 
webhook
(string)
python
insight_template_group_detail = client.ai.conversations.insight_groups.insight_groups(
    name="name",
)
print(insight_template_group_detail.data)

Get Insight Template Group

Get insight group by ID
GET /ai/conversations/insight-groups/{group_id}
python
insight_template_group_detail = client.ai.conversations.insight_groups.retrieve(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(insight_template_group_detail.data)

Update Insight Template Group

Update an insight template group
PUT /ai/conversations/insight-groups/{group_id}
Optional: 
description
(string), 
name
(string), 
webhook
(string)
python
insight_template_group_detail = client.ai.conversations.insight_groups.update(
    group_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(insight_template_group_detail.data)

Delete Insight Template Group

Delete insight group by ID
DELETE /ai/conversations/insight-groups/{group_id}
python
client.ai.conversations.insight_groups.delete(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)

Assign Insight Template To Group

Assign an insight to a group
POST /ai/conversations/insight-groups/{group_id}/insights/{insight_id}/assign
python
client.ai.conversations.insight_groups.insights.assign(
    insight_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    group_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)

Unassign Insight Template From Group

Remove an insight from a group
DELETE /ai/conversations/insight-groups/{group_id}/insights/{insight_id}/unassign
python
client.ai.conversations.insight_groups.insights.delete_unassign(
    insight_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    group_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)

Get Insight Templates

Get all insights
GET /ai/conversations/insights
python
page = client.ai.conversations.insights.list()
page = page.data[0]
print(page.id)

Create Insight Template

Create a new insight
POST /ai/conversations/insights
— Required: 
instructions
, 
name
Optional: 
json_schema
(object), 
webhook
(string)
python
insight_template_detail = client.ai.conversations.insights.create(
    instructions="instructions",
    name="name",
)
print(insight_template_detail.data)

Get Insight Template

Get insight by ID
GET /ai/conversations/insights/{insight_id}
python
insight_template_detail = client.ai.conversations.insights.retrieve(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(insight_template_detail.data)

Update Insight Template

Update an insight template
PUT /ai/conversations/insights/{insight_id}
Optional: 
instructions
(string), 
json_schema
(object), 
name
(string), 
webhook
(string)
python
insight_template_detail = client.ai.conversations.insights.update(
    insight_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(insight_template_detail.data)

Delete Insight Template

Delete insight by ID
DELETE /ai/conversations/insights/{insight_id}
python
client.ai.conversations.insights.delete(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)

Get a conversation

Retrieve a specific AI conversation by its ID.
GET /ai/conversations/{conversation_id}
python
conversation = client.ai.conversations.retrieve(
    "conversation_id",
)
print(conversation.data)

Update conversation metadata

Update metadata for a specific conversation.
PUT /ai/conversations/{conversation_id}
Optional: 
metadata
(object)
python
conversation = client.ai.conversations.update(
    conversation_id="conversation_id",
)
print(conversation.data)

Delete a conversation

Delete a specific conversation by its ID.
DELETE /ai/conversations/{conversation_id}
python
client.ai.conversations.delete(
    "conversation_id",
)

Get insights for a conversation

Retrieve insights for a specific conversation
GET /ai/conversations/{conversation_id}/conversations-insights
python
response = client.ai.conversations.retrieve_conversations_insights(
    "conversation_id",
)
print(response.data)

Create Message

Add a new message to the conversation.
POST /ai/conversations/{conversation_id}/message
— Required: 
role
Optional: 
content
(string), 
metadata
(object), 
name
(string), 
sent_at
(date-time), 
tool_call_id
(string), 
tool_calls
(array[object]), 
tool_choice
(object)
python
client.ai.conversations.add_message(
    conversation_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    role="role",
)

Get conversation messages

Retrieve messages for a specific conversation, including tool calls made by the assistant.
GET /ai/conversations/{conversation_id}/messages
python
messages = client.ai.conversations.messages.list(
    "conversation_id",
)
print(messages.data)

Get Tasks by Status

Retrieve tasks for the user that are either 
queued
, 
processing
, 
failed
, 
success
or 
partial_success
based on the query string.
GET /ai/embeddings
python
embeddings = client.ai.embeddings.list()
print(embeddings.data)

Embed documents

Perform embedding on a Telnyx Storage Bucket using an embedding model.
POST /ai/embeddings
— Required: 
bucket_name
Optional: 
document_chunk_overlap_size
(integer), 
document_chunk_size
(integer), 
embedding_model
(object), 
loader
(object)
python
embedding_response = client.ai.embeddings.create(
    bucket_name="bucket_name",
)
print(embedding_response.data)

List embedded buckets

Get all embedding buckets for a user.
GET /ai/embeddings/buckets
python
buckets = client.ai.embeddings.buckets.list()
print(buckets.data)

Get file-level embedding statuses for a bucket

Get all embedded files for a given user bucket, including their processing status.
GET /ai/embeddings/buckets/{bucket_name}
python
bucket = client.ai.embeddings.buckets.retrieve(
    "bucket_name",
)
print(bucket.data)

Disable AI for an Embedded Bucket

Deletes an entire bucket's embeddings and disables the bucket for AI-use, returning it to normal storage pricing.
DELETE /ai/embeddings/buckets/{bucket_name}
python
client.ai.embeddings.buckets.delete(
    "bucket_name",
)

Search for documents

Perform a similarity search on a Telnyx Storage Bucket, returning the most similar 
num_docs
document chunks to the query.
POST /ai/embeddings/similarity-search
— Required: 
bucket_name
, 
query
Optional: 
num_of_docs
(integer)
python
response = client.ai.embeddings.similarity_search(
    bucket_name="bucket_name",
    query="query",
)
print(response.data)

Embed URL content

Embed website content from a specified URL, including child pages up to 5 levels deep within the same domain.
POST /ai/embeddings/url
— Required: 
url
, 
bucket_name
python
embedding_response = client.ai.embeddings.url(
    bucket_name="bucket_name",
    url="url",
)
print(embedding_response.data)

Get an embedding task's status

Check the status of a current embedding task.
GET /ai/embeddings/{task_id}
python
embedding = client.ai.embeddings.retrieve(
    "task_id",
)
print(embedding.data)

List all clusters

GET /ai/clusters
python
page = client.ai.clusters.list()
page = page.data[0]
print(page.task_id)

Compute new clusters

Starts a background task to compute how the data in an embedded storage bucket is clustered.
POST /ai/clusters
— Required: 
bucket
Optional: 
files
(array[string]), 
min_cluster_size
(integer), 
min_subcluster_size
(integer), 
prefix
(string)
python
response = client.ai.clusters.compute(
    bucket="bucket",
)
print(response.data)

Fetch a cluster

GET /ai/clusters/{task_id}
python
cluster = client.ai.clusters.retrieve(
    task_id="task_id",
)
print(cluster.data)

Delete a cluster

DELETE /ai/clusters/{task_id}
python
client.ai.clusters.delete(
    "task_id",
)

Fetch a cluster visualization

GET /ai/clusters/{task_id}/graph
python
response = client.ai.clusters.fetch_graph(
    task_id="task_id",
)
print(response)
content = response.read()
print(content)

Transcribe speech to text

Transcribe speech to text.
POST /ai/audio/transcriptions
python
response = client.ai.audio.transcribe(
    model="distil-whisper/distil-large-v2",
)
print(response.text)

Create a chat completion

Chat with a language model.
POST /ai/chat/completions
— Required: 
messages
Optional: 
api_key_ref
(string), 
best_of
(integer), 
early_stopping
(boolean), 
frequency_penalty
(number), 
guided_choice
(array[string]), 
guided_json
(object), 
guided_regex
(string), 
length_penalty
(number), 
logprobs
(boolean), 
max_tokens
(integer), 
min_p
(number), 
model
(string), 
n
(number), 
presence_penalty
(number), 
response_format
(object), 
stream
(boolean), 
temperature
(number), 
tool_choice
(enum), 
tools
(array[object]), 
top_logprobs
(integer), 
top_p
(number), 
use_beam_search
(boolean)
python
response = client.ai.chat.create_completion(
    messages=[{
        "role": "system",
        "content": "You are a friendly chatbot.",
    }, {
        "role": "user",
        "content": "Hello, world!",
    }],
)
print(response)

List fine tuning jobs

Retrieve a list of all fine tuning jobs created by the user.
GET /ai/fine_tuning/jobs
python
jobs = client.ai.fine_tuning.jobs.list()
print(jobs.data)

Create a fine tuning job

Create a new fine tuning job.
POST /ai/fine_tuning/jobs
— Required: 
model
, 
training_file
Optional: 
hyperparameters
(object), 
suffix
(string)
python
fine_tuning_job = client.ai.fine_tuning.jobs.create(
    model="model",
    training_file="training_file",
)
print(fine_tuning_job.id)

Get a fine tuning job

Retrieve a fine tuning job by 
job_id
.
GET /ai/fine_tuning/jobs/{job_id}
python
fine_tuning_job = client.ai.fine_tuning.jobs.retrieve(
    "job_id",
)
print(fine_tuning_job.id)

Cancel a fine tuning job

Cancel a fine tuning job.
POST /ai/fine_tuning/jobs/{job_id}/cancel
python
fine_tuning_job = client.ai.fine_tuning.jobs.cancel(
    "job_id",
)
print(fine_tuning_job.id)

Create embeddings

Creates an embedding vector representing the input text.
POST /ai/openai/embeddings
— Required: 
input
, 
model
Optional: 
dimensions
(integer), 
encoding_format
(enum), 
user
(string)
python
response = client.ai.openai.embeddings.create_embeddings(
    input="The quick brown fox jumps over the lazy dog",
    model="thenlper/gte-large",
)
print(response.data)

List embedding models

Returns a list of available embedding models.
GET /ai/openai/embeddings/models
python
response = client.ai.openai.embeddings.list_embedding_models()
print(response.data)

Get available models

This endpoint returns a list of Open Source and OpenAI models that are available for use.
GET /ai/models
python
response = client.ai.retrieve_models()
print(response.data)

Summarize file content

Generate a summary of a file's contents.
POST /ai/summarize
— Required: 
bucket
, 
filename
Optional: 
system_prompt
(string)
python
response = client.ai.summarize(
    bucket="bucket",
    filename="filename",
)
print(response.data)